A combined transmembrane topology and signal peptide prediction method

Journal of Molecular Biology

Volumn 338, Issue 5, 2004, Pages 1027-1036

  Käll, Lukas ^a   Krogh, Anders ^b   Sonnhammer, Erik L L ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Hidden Markov model; HMM, hidden Markov model; Machine learning; Signal peptide; SPs, signal peptides; TM, transmembrane; Topology prediction; Transmembrane protein

Indexed keywords

MEMBRANE PROTEIN; NITROGEN; PEPTIDE; PROTEOME;

ANALYTIC METHOD; ARTICLE; CELL MEMBRANE; CROSS REACTION; CYTOPLASM; ESCHERICHIA COLI; GENOME; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; PROTEIN ASSEMBLY; PROTEIN LOCALIZATION; SIGNAL TRANSDUCTION;

ESCHERICHIA COLI; HOMO; HOMO SAPIENS;

EID: 2142657817     PISSN: 00222836     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmb.2004.03.016     Document Type: Article

References (40)

1. Krogh A., Larsson B., von Heijne G., Sonnhammer E.L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305:2001;567-580.
2. Tusnady G.E., Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J. Mol. Biol. 283:1998;489-506.
3. Lao D.M., Arai M., Ikeda M., Shimizu T. The presence of signal peptide significantly affects transmembrane topology prediction. Bioinformatics. 18:2002;1562-1566.
4. Nielsen H., Engelbrecht J., Brunak S., von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 10:1997;1-6.
5. Nielsen H., Krogh A. Prediction of signal peptides and signal anchors by a hidden Markov model. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6:1998;122-130.
6. Nielsen, H. (1999). From sequence to sorting: prediction of signal peptides. PhD Thesis, Stockholm University.
7. Kyte J., Doolittle R.F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157:1982;105-132.
8. Argos P., Rao J.K., Hargrave P.A. Structural prediction of membrane-bound proteins. Eur. J. Biochem. 128:1982;565-575.
9. von Heijne G. The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology. EMBO J. 5:1986;3021-3027.
10. von Heijne G. Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J. Mol. Biol. 225:1992;487-494.
11. Cserzo M., Wallin E., Simon I., von Heijne G., Elofsson A. Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Eng. 10:1997;673-676.
12. Rost B., Casadio R., Fariselli P., Sander C. Transmembrane helices predicted at 95% accuracy. Protein Sci. 4:1995;521-533.
13. Jones D.T., Taylor W.R., Thornton J.M. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry. 33:1994;3038-3049.
14. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE. 77:1989;257-286.
15. Sonnhammer E.L., von Heijne G., Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6:1998;175-182.
16. Martelli P.L., Fariselli P., Krogh A., Casadio R. A sequence-profile- based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics. 18:2002;S46-S53.
17. Zhai Y., Saier M.H. The beta-barrel finder (BBF) program, allowing identification of outer membrane beta-barrel proteins encoded within prokaryotic genomes. Protein Sci. 11:2002;2196-2207.
18. von Heijne G. Patterns of amino acids near signal-sequence cleavage sites. Eur. J. Biochem. 133:1983;17-21.
19. Perlman D., Halvorson H.O. A putative signal peptidase recognition site and sequence in eukaryotic and prokaryotic signal peptides. J. Mol. Biol. 167:1983;391-409.
20. von Heijne G. A new method for predicting signal sequence cleavage sites. Nucl. Acids Res. 14:1986;4683-4690.
21. Chou K.C. Prediction of protein signal sequences and their cleavage sites. Proteins: Struct. Funct. Genet. 42:2001;136-139.
22. Vert J.P. Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. Pac. Symp. Biocomput. 2002;649-660.
23. Menne K.M., Hermjakob H., Apweiler R. A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics. 16:2000;741-742.
24. Juncker A.S., Willenbrock H., von Heijne G., Brunak S., Nielsen H., Krogh A. Prediction of lipoprotein signal peptides in Gram-negative Bacteria. Protein Sci. 2004;. in the press.
25. Krogh A., Brown M., Mian I.S., Sjolander K., Haussler D. Hidden Markov models in computational biology. Applications to protein modeling. J. Mol. Biol. 235:1994;1501-1531.
26. Nielsen H., Engelbrecht J., Brunak S., von Heijne G. A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Int. J. Neural Syst. 8:1997;581-599.
27. Boeckmann B., Bairoch A., Apweiler R., Blatter M.C., Estreicher A., Gasteiger E., et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucl. Acids Res. 31:2003;365-370.
28. Arai M., Ikeda M., Shimizu T. Comprehensive analysis of transmembrane topologies in prokaryotic genomes. Gene. 304:2003;77-86.
29. Käll L., Sonnhammer E.L. Reliability of transmembrane predictions in whole-genome data. FEBS Letters. 532:2002;415-418.
30. Melen K., Krogh A., von Heijne G. Reliability measures for membrane protein topology prediction algorithms. J. Mol. Biol. 327:2003;735-744.
31. Lander E.S., Linton L.M., Birren B., Nusbaum C., Zody M.C., Baldwin J., et al. Initial sequencing and analysis of the human genome. Nature. 409:2001;860-921.
32. Blattner F.R., Plunkett G. III, Bloch C.A., Perna N.T., Burland V., Riley M., et al. The complete genome sequence of Escherichia coli K-12. Science. 277:1997;1453-1462.
33. Möller S., Croning M.D., Apweiler R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics. 17:2001;646-653.
34. Chen C.P., Kernytsky A., Rost B. Transmembrane helix predictions revisited. Protein Sci. 11:2002;2774-2791.
35. Ikeda M., Arai M., Okuno T., Shimizu T. TMPDB: a database of experimentally-characterized transmembrane topologies. Nucl. Acids Res. 31:2003;406-409.
36. Möller S., Kriventseva E.V., Apweiler R. A collection of well characterised integral membrane proteins. Bioinformatics. 16:2000;1159-1160.
37. Sonnhammer E.L., Durbin R. A workbench for large-scale sequence homology analysis. Comput. Appl. Biosci. 10:1994;301-307.
38. Hobohm U., Scharf M., Schneider R., Sander C. Selection of representative protein data sets. Protein Sci. 1:1992;409-417.
39. Krogh A. Hidden Markov models for labeled sequences. Proceedings of the 12th IAPR International Conference on Pattern Recognition. 1994;IEEE Computer Society Press, Los Alamitos, CA. pp. 140-144.
40. Schwartz R., Chow Y. The N-best algorithm: an efficient and exact procedure for finding the N most likely sentence hypotheses. Proc. ICASSP, Albuquerque, NM. 1990:1990;81-84.